#Class 05: Data Visualization
#Today we are going to use ggplot2 package
#First we need to load the package!
#install.packages("ggplot2") - this is a one-time only installation!
library(ggplot2)
#We will use this in-built "cars" dataset first
head(cars)
## speed dist
## 1 4 2
## 2 4 10
## 3 7 4
## 4 7 22
## 5 8 16
## 6 9 10
#All ggplots have at least 3 layers;
#data + aes + geoms
ggplot(data=cars) +
aes(x=speed, y=dist) +
geom_point() +
geom_smooth(method="lm") +
labs(title="Stopping Distance of Old Cars",
x="Speed (MPH)",
y="Stopping Distance (ft)")
## `geom_smooth()` using formula 'y ~ x'

#Grey area around line = SEM
#Side note: ggplot is not the only graphics system
#A very popular one is good old "base" R graphics
plot(cars)

plot(cars$speed, cars$speed)

#Follow this same process as directed in the worksheet for lab 5. Now, we will be looking at gene expression changes upon drug treatment
url <- "https://bioboot.github.io/bimm143_S20/class-material/up_down_expression.txt"
genes <- read.delim(url)
head(genes)
## Gene Condition1 Condition2 State
## 1 A4GNT -3.6808610 -3.4401355 unchanging
## 2 AAAS 4.5479580 4.3864126 unchanging
## 3 AASDH 3.7190695 3.4787276 unchanging
## 4 AATF 5.0784720 5.0151916 unchanging
## 5 AATK 0.4711421 0.5598642 unchanging
## 6 AB015752.4 -3.6808610 -3.5921390 unchanging
p <- ggplot(data=genes) +
aes(x=Condition1, y=Condition2, col=State) +
geom_point() +
scale_color_manual(values=c("blue", "gray", "red")) +
labs(title="Gene Expression Changes Upon Drug Treatment",
x="Control (No Drug)",
y="Drug Treatment")
#Question: How many genes are in this dataset?
nrow(genes)
## [1] 5196
#Question: How many genes are "up"?
table(genes$State)
##
## down unchanging up
## 72 4997 127
#Question: What % are up?
table(genes$State)/nrow(genes)
##
## down unchanging up
## 0.01385681 0.96170131 0.02444188
#Question: How do we report this as a percentage?
table(genes$State)/nrow(genes) * 100
##
## down unchanging up
## 1.385681 96.170131 2.444188
#Question: How do we round off to a specific number of sig figs?
round(table(genes$State)/nrow(genes) * 100, 3)
##
## down unchanging up
## 1.386 96.170 2.444
#See lines 29-35 to make a figure
#General notes on making the figure:
p <- ggplot(genes) +
aes(x=Condition1, y=Condition2, col=State) +
geom_point()
p

# I like it but not the default colors, let's change them
p + scale_color_manual(values=c("blue", "gray", "red"))

#Section of lab: Going Further
library(gapminder)
library(dplyr)
##
## Attaching package: 'dplyr'
## The following objects are masked from 'package:stats':
##
## filter, lag
## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, union
gapminder_2007 <- gapminder %>% filter(year==2007)
#Let's make another figure - first, a scatterplot!
ggplot(gapminder_2007) +
aes(x=gdpPercap, y=lifeExp) +
geom_point(alpha=0.5)

#The "alpha" value will make the points on the scatterplot more transparent
#To add even more complexity to the plot, we will add the following to our calls:
ggplot(gapminder_2007) +
aes(x=gdpPercap, y=lifeExp, color=continent, size=pop) +
geom_point(alpha=0.5)

#Now try this...
ggplot(gapminder_2007) +
aes(x=gdpPercap, y=lifeExp, color=pop) +
geom_point(alpha=0.8)

#Now try this...
ggplot(gapminder_2007) +
aes(x=gdpPercap, y=lifeExp, size=pop) +
geom_point(alpha=0.5)

#To add scaling information:
ggplot(gapminder_2007) +
aes(x=gdpPercap, y=lifeExp, size=pop) +
geom_point(alpha=0.5) +
scale_size_area(max_size = 10)

#Now, let's try putting this all together to generate a new figure:
gapminder_1957 <- gapminder %>% filter(year==1957)
ggplot(gapminder_1957)

ggplot(gapminder_1957) +
aes(x=gdpPercap, y=lifeExp, color=continent) +
geom_point()

ggplot(gapminder_1957) +
aes(x=gdpPercap, y=lifeExp, color=continent, size=pop) +
geom_point()

ggplot(gapminder_1957) +
aes(x=gdpPercap, y=lifeExp, color=continent) +
geom_point(alpha=0.7) +
scale_size_area(max_size = 15)

ggplot(gapminder_1957) +
aes(x=gdpPercap, y=lifeExp, color=continent) +
geom_point(alpha=0.7) +
scale_size_area(max_size = 15) +
facet_wrap(~year)

#Let's make a new plot of year vs. lifeExp
ggplot(gapminder) +
aes(x=year, y=lifeExp, col=continent) +
geom_jitter(width=0.3,alpha=0.4) +
geom_boxplot(aes(group=year, alpha=0.2))

ggplot(gapminder) +
aes(x=year, y=lifeExp, col=continent) +
geom_jitter(width=0.3,alpha=0.4) +
geom_violin(aes(group=year), alpha=0.2,
draw_quantiles=0.5)

#Install the plotly
#Install.packages("plotly")
library(plotly)
##
## Attaching package: 'plotly'
## The following object is masked from 'package:ggplot2':
##
## last_plot
## The following object is masked from 'package:stats':
##
## filter
## The following object is masked from 'package:graphics':
##
## layout
ggplotly()